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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qi Du Feng Wang Hui Huang Heng Wan Xiaoyu Wu Chengkun Wu College of Computer Science and Electronic Engineering Hunan University Changsha China Hunan Institute of Scientific and Technical Information Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

In molecular dynamics simulations, the integral methods used to calculate molecular trajectories, such as the Verlet integral method, involve significant computational costs. On the premise of ensuring the accuracy of simulation, how to effectively reduce the amount of computation is always a challenging problem. This paper applies Natural Language Processing models to simulate molecular motion trajectories in molecular dynamics, integrating the MPI programming model and machine learning on a high-performance computing platform to achieve MPI parallelization during model training and inference. The results indicate that different types of neural network architectures have a significant impact on inference performance and accuracy. When using the deep spatio-temporal networks model, the mean absolute error is 0.0032. At an atomic scale of 3M, the parallel efficiency with 1024 computational nodes exceeds 90%, demonstrating excellent parallel performance.

关键词： Training Accuracy Computational modeling High performance computing Neural networks Machine learning computer architecture Natural language processing Trajectory Computational efficiency

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Design of Multimedia Database Online Integration System Based on Artificial Intelligence

Design of Multimedia Database Online Integration System Base...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Jiaqi Li Gaojie Xiong Xing Wu Computer Sciense and Technology Harbin Institute of Technology Shenzhen Guangdong China School of Information and Software Engineering University of Electronic Science and Technology Chengdu Sichuan China Zhejiang Vocational Academy of Art Hangzhou Zhejiang China

Multimedia database online integration system plays an important role in today’s information age. However, the performance of the integrated system is limited by the problems of low accuracy, insufficient recall rate and long running time of traditional methods in processing multimedia data. This paper designs an online integration system based on artificial intelligence (AI) and verifies its effectiveness and feasibility through experiments so as to overcome these problems. In the study, multimedia datasets suitable for the experimental needs are selected and the AI-based online integration system is compared with traditional methods. Through the analysis of accuracy, recall, F1 value and mean square error, it is found that the online integration system based on AI has obvious advantages in multimedia data integration. The results of this paper demonstrate the feasibility and effectiveness of an online integration system based on AI. It is able to enhance the accuracy and efficiency of multimedia data integration while reducing runtime and resource consumption.

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Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber Sensor: A Machine Learning Approach

Ultra-Fast Core Mode Prediction in Plasmonic Crystal Fiber S...

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International Conference on Innovations in science, engineering and Technology (ICISET)

作者： Mahmudul Hoque Mahmud Ariful Hoque Maruf Mohammad Nasir Uddin Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Software Engineering Institute of Information Technology University of Dhaka Dhaka Bangladesh Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

This study introduces a novel approach to improving Photonic Crystal Fiber (PCF) Surface Plasmon Resonance (SPR) sensors by incorporating machine learning (ML) models for predicting core mode index. Traditionally, identifying core phenomena in PCF SPR sensors required manual visual inspection by experts, a process that is both time-consuming and prone to errors. To address this, a dual-core PCF model is developed and used Finite Element Method (FEM) simulations to create extensive training data for ML models. The proposed PCF model shows exceptional sensitivity to wavelength changes, with a recorded sensitivity of $\mathbf{6 0 0 0} \mathrm{nm} /$ RIU, making it highly effective for accurate mode prediction. The study evaluated nine ML models, including XGBoost, Random Forest, and Decision Tree, achieving accuracies of 0.9742,0.9645, and 0.9667, respectively. By integrating ML models, the core mode identification process is significantly faster and more efficient, offering major benefits for PCF SPR research. This work not only advances PCF sensor technology but also enhances efficiency and accuracy in various SPR-based applications.

关键词： Accuracy Sensitivity Photonic crystal fibers Predictive models Sensor phenomena and characterization Surface plasmon resonance Data models Sensors Finite element analysis Numerical models

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S-Faster R-CNN: Intra-Spectral Similarity Learning for Audio Copy-Move Forgery Localization in IoT Security

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IEEE Internet of Things Journal 2025年

作者： Qiu, Xin Shi, Canghong Li, Xiaojie Wu, Min Abdullahi, Sani M. Liu, Yong Xihua University School of Computer and Software Engineering Chengdu610039 China Chengdu University of Information Technology School of Computer Science Chengdu610225 China University of Southern Denmark M Rsk McKinney Moller Institute Odense5230 Denmark Shanghai University School of Communication and Information Systems 200444 China

In the Internet of Audio Things, communication security of the audio control terminal is vulnerable to copy-move threats, and detecting and locating audio copy-move forgery remains challenging nowadays. The forgery detection method based on deep learning achieves higher detection accuracy but fails to localize forged regions. To address this issue, this article proposes an S-Faster R-CNN model for audio copy-move forgery detection and localization. We integrate a novel Similarity Computation Module (SCM) into the Faster R-CNN framework, forming the S-Faster R-CNN model. Obtaining the integration of the SCM, which allows the S-Faster R-CNN to precisely localize forgery regions within the spectrogram. Finally, the image coordinate transformation algorithm is used to map these forged regions to the corresponding locations of the original audio waveform, thus completing the audio copy-move forgery detection and localization. Evaluated on three datasets, our method achieves an average recall of 90%, an average precision of 84%, and an average F1-score of 87%, respectively. Experimental results indicate that the S-Faster R-CNN outperforms state-of-the-art methods in both forgery detection accuracy and especially in localization. Moreover, the proposed method shows good robustness under multiple post-processing. © 2014 IEEE.

关键词： Spectrographs

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An IoT-Based Air Pollution Monitoring System for Smart City 4

An IoT-Based Air Pollution Monitoring System for Smart City

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4th International Conference on computer Systems, ICCS 2024

作者： Aubakirov, Alibek Makhanov, Kanat Issainova, Aliya Glazyrina, Natalya Razkhova, Bibigul Satybaldina, Dina School of Computer Science Hangzhou Dianzi University Hangzhou China Electronics and Telecommunications L.N Gumilyov Eurasian National University Department of Radio Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Information Security Department Astana Kazakhstan L.N Gumilyov Eurasian National University Computer and Software Engineering Department Astana Kazakhstan L.N. Gumilyov Eurasian National University Department of Artificial Astana Kazakhstan Information Security and Cryptology Research Institute L.N. Gumilyov Eurasian National University Astana Kazakhstan

ISBN: (纸本)9798350366341

In this paper, we present the results of research on the design and development of a gas analytical unit, an electronic nose, with an autonomous power supply, as well as the function of wireless data transmission via the Long-Range Wide Area Networks protocol. We built the device on a circuit board featuring eight gas sensors (MQ series), an STM32F103C8T6 microcontroller with 12-bit analog-to-digital conversion, a RAK3172 wireless data transmission module, voltage dividers, and a USB serial adapter. It also has an HDC2080DMBR temperature and humidity sensor. We evaluated the electronic nose performance towards standard mixtures of carbon dioxide, carbon monoxide, nitrogen dioxide, ammonia, and hydrogen sulfide, which are important analytes in outdoor and indoor air quality monitoring. Multidimensional linear regression methods were employed to build calibration curves for electronic nose sensors, which allowed for converting the measured sensor responses into quantitative values of the concentrations of the analyzed substances. The estimated coefficients of determination, R2, approach 1 indicating a strong correlation between the approximating model and the original data. The multivariate chemiresistive responses were successfully clustered using the k-means algorithm, revealing clear distinctions between sensor data associated with different gas concentrations. The k-NN classifier, applied to the clustered data, achieved 100% accuracy in identifying gas concentrations, underscoring the potential of the proposed methodology for gas sensing applications. We proposed and implemented the architecture of a network of mobile gas analyzers as the Internet of Things devices for environmental monitoring using the VK Cloud Platform. © 2024 IEEE.

关键词： Analog to digital conversion

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Federated Learning with Only Positive Labels by Exploring Label Correlations

arXiv

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arXiv 2024年

作者： An, Xuming Wang, Dui Shen, Li Luo, Yong Hu, Han Du, Bo Wen, Yonggang Tao, Dacheng School of Information and Electronics Beijing Institute of Technology Beijing100081 China National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China JD Explore Academy Beijing100000 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification problem under the federated learning setting, where trivial solution and extremely poor performance may be obtained, especially when only positive data w.r.t. a single class label are provided for each client. This issue can be addressed by adding a specially designed regularizer on the server-side. Although effective sometimes, the label correlations are simply ignored and thus sub-optimal performance may be obtained. Besides, it is expensive and unsafe to exchange user’s private embeddings between server and clients frequently, especially when training model in the contrastive way. To remedy these drawbacks, we propose a novel and generic method termed Federated Averaging by exploring Label Correlations (FedALC). Specifically, FedALC estimates the label correlations in the class embedding learning for different label pairs and utilizes it to improve the model training. To further improve the safety and also reduce the communication overhead, we propose a variant to learn fixed class embedding for each client, so that the server and clients only need to exchange class embeddings once. Extensive experiments on multiple popular datasets demonstrate that our FedALC can significantly outperform existing counterparts. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

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computers, Materials & Continua 2021年第6期67卷 3933-3948页

作者： Xiaorui Zhang Xuan Chen Wei Sun Xiaozheng He Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Civil and Environmental Engineering Rensselaer Polytechnic InstituteTroyNY12180USA

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

关键词： Vehicle re-identication densenet joint loss focal loss SE block

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Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization

arXiv

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arXiv 2025年

作者： Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences China Computer Network Information Center Chinese Academy of Sciences China Department of Electrical Engineering and Computer Science University of Kansas United States School of Computing and Information Technology Great Bay University Dongguan China School of Software Tsinghua University China Institute for Infocomm Research Agency for Science Technology and Research Singapore

Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework’s core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operation, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Robust Dynamic Broadcasting for Multi-Hop Wireless Networks Under Time-Varying Connectivity and Dynamic SINR

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IEEE Transactions on Mobile Computing 2025年

作者： Tian, Xiang Yu, Jiguo Luo, Chuanwen Yu, Dongxiao Wang, Guijuan Feng, Bin Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Taishan University School of Information Science and Technology 271000 China

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations. © 2002-2012 IEEE.

关键词： Signal to noise ratio

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques

A Mathematical Model for the Selection of Software Requireme...

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International Conference on Confluence The Next Generation information Technology Summit (Confluence)

作者： Virat Raj Saxena Rajeev Yadav Azra Parveen Mohd. Sadiq Department of Computer Science and Engineering Mangalayatan University Aligarh Uttar Pradesh India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi India

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model.

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